1. Field of the Invention
The present invention relates generally to information processing systems that are used to identify unknown parameters in a system, and more specifically to an information processing system that monitors motion-related sensors to accurately identify spacecraft mass-properties. The general field is commonly known as System Identification (ID).
2. Prior Art
The use of linear least squares regression for the identification of unknown system parameters has been used and studied extensively, as by Lawson, C. and Hanson, R., in Solving Least Squares Problems, 1974, and Ljung, L., in System Identification, Theory for the User, 1999. However, the requirement that a regression equation be formed with the unknown parameters linearly represented, limits its direct applicability to many important problems, including the spacecraft application presented here.
Ljung and several other authors have developed approaches for identification of parameters in highly non-linear systems using methods such as gradient-based optimization and neural networks. However, these methods are significantly more computationally intensive than those for linear problems.
The remaining prior art items relate more specifically to the spacecraft mass-property ID aspect of the invention. In that problem, some of the unknown parameters multiply each other in the governing equations, resulting in a specific type of nonlinearity.
Tanygin, S. and Williams, T., in “Mass property estimation using coasting maneuvers,” Journal of Guidance, Control, and Dynamics, 1997, developed a least squares (LS) based algorithm to identify mass properties for a spinning vehicle during coasting maneuvers. The restriction to the case of a spinning spacecraft with no applied torques or thrusters firing limits its applicability considerably—either to spacecraft that normally exist in this state, or by requiring other spacecraft to attain this state.
Bergmann, E., et al., in “Mass property estimation for control of asymmetrical satellites,” Journal of Guidance, Control, and Dynamics, 1987, developed an ID approach using a Gaussian second-order filter as presented more generally by Gelb, A, et al., in Applied Optimal Estimation, 1974. The second order filter resembles an extended Kalman filter, but has extra terms to address the second order effects. This is significantly more complex and computationally intensive (by about two orders of magnitude) than the approach presented here, and may not produce better results for most spacecraft. The extra complexity may make it more susceptible to noise and parameter variations than the presented methods. It assumes perfect knowledge of thruster properties.
Wilson, E. and Rock, S. M., in “Reconfigurable control of a free-flying space robot using neural networks,” Proceedings of the American Control Conference, 1995, developed an ID method based on exponentially weighted RLS using accelerometer and angular rate sensors. The acceleration created by each thruster (reflecting both mass and thruster properties) was identified. This approach (identifying thruster acceleration rather than separately identifying mass and thruster properties) is more direct (since thruster acceleration is the real value of interest from a control, estimation, or FDI standpoint), and probably better for vehicles with properties that are truly unknown (such as for the case where deflected thrusters are allowed, such as on the vehicle tested in that research). However, for most vehicles, certain properties are well known, such as the thrust directions and locations in the structural frame. The present invention can take advantage of that knowledge to get better estimates of the properties that are not well known.